Learning to Recognize Chest-Xray Images Faster and More Efficiently Based on Multi-Kernel Depthwise Convolution
The development of convolutional neural networks has promoted the progress of computer-aided diagnostic systems. Details in medical image, such as the texture and tissue structure, are crucial features for diagnosis. Therefore, large input images combined with deep convolution neural networks are ad...
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doaj-cd6fc7ae90364a4aae9b0eb16623203e2021-03-30T02:31:04ZengIEEEIEEE Access2169-35362020-01-018372653727410.1109/ACCESS.2020.29742429000602Learning to Recognize Chest-Xray Images Faster and More Efficiently Based on Multi-Kernel Depthwise ConvolutionMengjie Hu0https://orcid.org/0000-0001-7712-3322Hezheng Lin1Zimeng Fan2Wenjie Gao3Lu Yang4Chun Liu5https://orcid.org/0000-0002-2834-9461Qing Song6https://orcid.org/0000-0003-4616-2200Pattern Recognition and Intelligent Vision Laboratory, Beijing University of Posts and Telecommunications, Beijing, ChinaPattern Recognition and Intelligent Vision Laboratory, Beijing University of Posts and Telecommunications, Beijing, ChinaPattern Recognition and Intelligent Vision Laboratory, Beijing University of Posts and Telecommunications, Beijing, ChinaPattern Recognition and Intelligent Vision Laboratory, Beijing University of Posts and Telecommunications, Beijing, ChinaPattern Recognition and Intelligent Vision Laboratory, Beijing University of Posts and Telecommunications, Beijing, ChinaPattern Recognition and Intelligent Vision Laboratory, Beijing University of Posts and Telecommunications, Beijing, ChinaPattern Recognition and Intelligent Vision Laboratory, Beijing University of Posts and Telecommunications, Beijing, ChinaThe development of convolutional neural networks has promoted the progress of computer-aided diagnostic systems. Details in medical image, such as the texture and tissue structure, are crucial features for diagnosis. Therefore, large input images combined with deep convolution neural networks are adopted to boost the performance in recent research of chest X-ray diagnosis. Meanwhile, due to the variable sizes of thoracic diseases, many researchers have worked to introduce additional module to capture multi-scale feature of images in CNN. However, these efforts hardly consider the computational costs of large inputs and introduced additional modules. This paper aims to automatically diagnose diseases on chest X-rays images quickly and effectively. We propose the multi-kernel depthwise convolution(MD-Conv) which contains depthwise convolution kernels with different filter sizes in one depthwise convolution layer. MD-Conv has high calculation efficiency and few parameters. Because its ability to learn multi-scale feature based on the multi-size kernels, it is appropriate for medical images diagnosis tasks in which abnormalities varied in sizes. In addition, larger depthwise convolution kernels are adopted in MD-Conv to obtain a larger receptive field efficiently, which can ensure sufficient receptive field for high resolution inputs. MD-Conv can be easily applied in modern lightweight networks to replace the normal depthwise convolution layer. We conduct experiments on the Chest X-ray 14 Dataset, which is the largest available chest x-ray dataset, and obtain competitive results. We also evaluate the MD-Conv on the new released dataset for pediatric pneumonia diagnosis. We obtain a better performance of 98.3% AUC than original paper (96.8%) for recognize pneumonia versus normal. Meanwhile we compare the FLOPs and Params of different models to show their efficiency for chest X-rays recognition.https://ieeexplore.ieee.org/document/9000602/Chest x-ray recognitionlightweight networksmulti-kernels depthwise convolution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mengjie Hu Hezheng Lin Zimeng Fan Wenjie Gao Lu Yang Chun Liu Qing Song |
spellingShingle |
Mengjie Hu Hezheng Lin Zimeng Fan Wenjie Gao Lu Yang Chun Liu Qing Song Learning to Recognize Chest-Xray Images Faster and More Efficiently Based on Multi-Kernel Depthwise Convolution IEEE Access Chest x-ray recognition lightweight networks multi-kernels depthwise convolution |
author_facet |
Mengjie Hu Hezheng Lin Zimeng Fan Wenjie Gao Lu Yang Chun Liu Qing Song |
author_sort |
Mengjie Hu |
title |
Learning to Recognize Chest-Xray Images Faster and More Efficiently Based on Multi-Kernel Depthwise Convolution |
title_short |
Learning to Recognize Chest-Xray Images Faster and More Efficiently Based on Multi-Kernel Depthwise Convolution |
title_full |
Learning to Recognize Chest-Xray Images Faster and More Efficiently Based on Multi-Kernel Depthwise Convolution |
title_fullStr |
Learning to Recognize Chest-Xray Images Faster and More Efficiently Based on Multi-Kernel Depthwise Convolution |
title_full_unstemmed |
Learning to Recognize Chest-Xray Images Faster and More Efficiently Based on Multi-Kernel Depthwise Convolution |
title_sort |
learning to recognize chest-xray images faster and more efficiently based on multi-kernel depthwise convolution |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
The development of convolutional neural networks has promoted the progress of computer-aided diagnostic systems. Details in medical image, such as the texture and tissue structure, are crucial features for diagnosis. Therefore, large input images combined with deep convolution neural networks are adopted to boost the performance in recent research of chest X-ray diagnosis. Meanwhile, due to the variable sizes of thoracic diseases, many researchers have worked to introduce additional module to capture multi-scale feature of images in CNN. However, these efforts hardly consider the computational costs of large inputs and introduced additional modules. This paper aims to automatically diagnose diseases on chest X-rays images quickly and effectively. We propose the multi-kernel depthwise convolution(MD-Conv) which contains depthwise convolution kernels with different filter sizes in one depthwise convolution layer. MD-Conv has high calculation efficiency and few parameters. Because its ability to learn multi-scale feature based on the multi-size kernels, it is appropriate for medical images diagnosis tasks in which abnormalities varied in sizes. In addition, larger depthwise convolution kernels are adopted in MD-Conv to obtain a larger receptive field efficiently, which can ensure sufficient receptive field for high resolution inputs. MD-Conv can be easily applied in modern lightweight networks to replace the normal depthwise convolution layer. We conduct experiments on the Chest X-ray 14 Dataset, which is the largest available chest x-ray dataset, and obtain competitive results. We also evaluate the MD-Conv on the new released dataset for pediatric pneumonia diagnosis. We obtain a better performance of 98.3% AUC than original paper (96.8%) for recognize pneumonia versus normal. Meanwhile we compare the FLOPs and Params of different models to show their efficiency for chest X-rays recognition. |
topic |
Chest x-ray recognition lightweight networks multi-kernels depthwise convolution |
url |
https://ieeexplore.ieee.org/document/9000602/ |
work_keys_str_mv |
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